Methods and apparatus to analyze recordings in leak detection

ABSTRACT

Methods and apparatus to analyze recordings in leak detection are disclosed. An example apparatus includes a leak detection sensor to record a plurality of recordings and a memory. The example apparatus also includes a processor to convert one ore of the plurality of recordings to a corresponding one or more spectral representations, calculate a spectral average based, at least in part, on the one or more spectral representations, store the spectral average to the memory, and generate a data packet based, at least in part, on the spectral average. The example apparatus also includes a transceiver to transmit the data packet to another device.

RELATED APPLICATION

This application claims the benefit under 35 U.S.C. § 119(e) to U.S.Provisional Application 62/466,843 titled “METHODS AND APPARATUS TOANALYZE RECORDINGS IN LEAK DETECTION,” filed Mar. 3, 2017, which isincorporated herein by this reference in its entirety.

FIELD OF THE DISCLOSURE

This disclosure relates generally to leak detection and, moreparticularly, to methods and apparatus to analyze recordings in leakdetection.

BACKGROUND

Some known leak detectors employ acoustic sensors to detect noise and/orcharacteristic sounds, which may be indicative of a potential leak. Inparticular, these leak detectors are usually coupled to a pipe and/orportion of a fluid delivery system and typically utilize amplitudeand/or a time-history of acoustic signals to determine a presence of apotential leak. However, many known leak detectors do not analyze and/orcharacterize waveforms in such an analysis.

In particular, the known leak detectors do not taken into accountambient/environmental noise, which can mask and/or provide falseindications of a leak. Further, many known leak detection systems and/orsensors cannot distinguish characteristic noise that may be inherent ina particular system from leak noise(s). As a result, these leakdetectors may be inaccurate and/or inherently lack capabilities ofcharacterizing a condition of a respective fluid delivery system.

SUMMARY

An example apparatus includes a leak detection sensor to record aplurality of recordings and a memory. The example apparatus alsoincludes a processor to convert one or more of the plurality ofrecordings to a corresponding one or more spectral representations,calculate a spectral average based, at least in part, on the one or morespectral representations, store the spectral average to the memory, andgenerate a data packet based, at least in part, on the spectral average.The example apparatus also includes a transceiver to transmit the datapacket to another device.

In some examples, the other device is a remote server or an endpointcoupled to a utility meter. In some examples, the endpoint or remoteserver is to compare information in the data packet to a baselinespectral average to determine a leak condition. In some examples, theprocessor is to compare the spectral average to a previously storedbaseline spectral average to determine a leak condition.

In some examples, the comparison includes subtracting the previouslystored baseline spectral average from the spectral average. In someexamples, the processor is to reject one or more of the plurality ofrecordings or the one or more spectral representations prior tocalculating the spectral average. In some examples, the processor is todirect the leak detection sensor to record additional recordings based,at least in part, on the rejection of the one or more of the pluralityof recordings.

In some examples, the processor is to update a previously storedbaseline spectral average based, at least in part, on the spectralaverage. In some examples, the example apparatus also includes a batteryoperatively coupled to the processor and the leak detection sensor. Insome examples, the example apparatus also includes circuitry to receivepower from an external device.

In some examples, the processor is to select one or more times to recordthe plurality of recordings based, at least in part, on one or more of atime of day, one or more previous recordings, and one or more previousspectral representations. In some examples, the processor is to transmitthe selected one or more times to record the plurality of recordings toan external device.

In some examples, the leak detection sensor includes an accelerometer.In some examples, the transceiver is to receive a known signature, wherethe processor is to make a comparison based at least in part on thespectral average and the known signature, and the processor is todetermine a leak condition based, at least in part, on the comparison.In some examples, a previously stored baseline spectral average issubtracted from the spectral average to make the comparison.

In some examples, the processor is to exit an off power state based upona supply of power from an external device and to return to the off powerstate after transceiver transmits the packet. In some examples, theprocessor is to encode a bias flag into the data packet based, at leastin part, on a number of recordings of the plurality of recordings beingless than a threshold number.

In some examples, the memory includes non-volatile memory. In someexamples, a fluid utility measuring device includes the exampleapparatus.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a schematic overview of an example utility measuring system inwhich the examples disclosed herein may be implemented.

FIG. 2 is a transparent view illustrating an example sensing module ofthe example utility measuring system of FIG. 1.

FIG. 3 is an exploded view of the example sensing module shown in FIGS.1 and 2.

FIG. 4 is an example plot representing example signal acquisition andanalysis in accordance with the teachings of this disclosure.

FIG. 5 is a graph representing a signal recording comparison that may beimplemented in the examples disclosed herein.

FIG. 6 illustrates an example averaging process that may be implementedin the examples disclosed herein.

FIG. 7 is a schematic overview of an analysis module of FIG. 1 that maybe used to implement the examples disclosed herein.

FIG. 8 is a flowchart representative of machine readable instructionsthat may be executed by the example analysis module of FIGS. 1 and 7.

FIG. 9 is a processor platform that may be used to execute the exampleinstructions of FIG. 8 to implement the example analysis module of FIGS.1 and 7.

The figures are not to scale. Instead, to clarify multiple layers andregions, the thickness of the layers may be enlarged in the drawings.Wherever possible, the same reference numbers will be used throughoutthe drawing(s) and accompanying written description to refer to the sameor like parts.

DETAILED DESCRIPTION

Methods and apparatus to analyze recordings in leak detection aredisclosed. Known leak detectors and/or systems often use sound amplitudeto determine a presence of a leak in a fluid delivery system. However,many of these known systems cannot distinguish characteristic acousticmeasurements and/or noise inherent in a particular system, therebyrendering potential inaccuracies in such systems. Further, known systemsmay be susceptible to false alarms and/or undetected leaks resultingfrom environmental and/or background noise.

The examples disclosed herein enable very accurate determination of leakconditions by removing uncertainty typically present in leak detectionsystems. The examples disclosed herein provide an effective and accuratemanner of determining leak conditions and/or a presence of a leak influid utility measuring systems by characterizing inherent properties ofsuch systems, which may pertain to environment, implementation and/ortypical use. In particular, the examples disclosed herein utilizelearning to generate spectral averages (e.g., baseline noise profiles,baseline spectral averages) that are unique to each particular system.Accordingly, these baseline measurements are collected over time (e.g.,a few hours, a few weeks, a months, etc.) to fully characterize uniqueacoustic recordings/signatures inherent for a corresponding system. As aresult, the examples disclosed herein account for random, environmental,usage and/or event-related noise.

The examples disclosed herein utilize a leak detection sensor, which maybe utilized as an acoustic sensor or a pressure sensor, to record ormeasure multiple recordings (e.g., recording signatures) that areconverted to spectral representations and, in turn, the spectralrepresentations are used to generate spectral averages.

According to the examples disclosed herein, the leak detection sensor isto record or measure recordings that are converted spectralrepresentations to generate a spectral average of the recordings. Inparticular, a current spectral average (e.g., a recent spectral average,today's spectral average, etc.) may be generated, for example. In turn,the current spectral average may be packaged or encoded into a datapacket that is transmitted to be compared to a baseline spectral averageto determine a leak condition. In some examples, this comparison occursat a remote server. In other examples, this comparison occurs at theleak detection sensor itself.

In some examples, recordings may be recorded during a defined or knowntime period (e.g., within a certain time of installation) to define theaforementioned baseline spectral average such that baselinecharacteristics of an individual system (e.g., a node of a utilitysystem, a utility system, a utility measuring system) are determined. Inparticular, the plurality of baseline recordings may be averaged bycanceling and/or reducing noise present in the plurality of baselinerecordings. These determined baseline characteristics are factored intoleak determination(s), thereby greatly increasing accuracy thereof.

In some examples, the current spectral average is compared to a knownsignature (e.g., a known leak signature), which may be downloaded and/orreceived via a network. In such examples, determination of the leakcondition may be at least partially based on this comparison between theknown signature and the current spectral average. For example, thesignatures and/or a database of the signatures may be stored innon-volatile memory corresponding to the leak detection sensor. Inparticular, the database may be referenced when analyzing/comparing thecurrent spectral average.

In some examples, adaptive sampling/recording is performed by the leakdetection sensor to vary a data acquisition mode of the leak detectionsensor. In particular, a polling rate, a power use mode and/or powerprovided (e.g., an amount of power) to the leak detection sensor may bevaried based on parameters related to a current recording and/or adifference between the current spectral average and the baselinespectral average. For example, an amplitude and/or a significant shiftfrom the baseline spectral average may trigger a change in a power modeof the leak detection sensor.

Additionally or alternatively, a polling rate (e.g., a scan rate) of theleak detection sensor may be varied based on the current recording(e.g., the current recording exceeds a threshold amplitude) and/or thecurrent spectral average. Additionally or alternatively, operationand/or parameters (e.g., power use mode(s), polling rates, etc.) of theleak detection sensor may be varied based on a time of day (e.g., theleak detection sensor is triggered from 1 am to 4 am based on lack ofexternal noises during that time, etc.). In some examples, the leakdetection sensor transmits a determined time of day to an externaldevice (e.g., the leak detection sensor transmits the determined time ofday to an endpoint so that the endpoint may “wake” the leak detectionsensor).

Additionally or alternatively, the data acquisition mode and/orrecording parameters may change within a session as a pattern isdetected (e.g., a step change from past historical recordings) withinthe session to the current recording. Additionally or alternatively,factors such as pipe diameter, material, fluid pipe content, whether thepipe is carrying fluid or gas, and/or temperature (e.g., environmentaltemperature, pipe temperature, fluid temperature, etc.) varies anoperating mode of the leak detection sensor (e.g., polling rate,sampling rate, signal filtering, recording method, etc.).

As used herein, the term “recording” refers to a measured or recordedsignal or time-history that corresponds to a time period (e.g., apre-defined time span). Accordingly, the term “recording” may be storedtemporarily (e.g., in random access memory) or in a tangible medium, andmay be represented or characterized over a frequency domain, forexample. As used herein, the term “spectral average” refers to anaveraged signal waveform.

As used herein, the terms “current recording” and “current spectralaverage” refer to a signal, plurality of signals and/or averaged signalsdefined or computed subsequent to a baseline characterization period. Asused herein, the terms “baseline recording” or “baseline spectralaverage” refer to a signal, plurality of signals and/or averaged signalsdefined or computed during a known time period and/or during a learningtime period (e.g., a time period around an installation time period).

FIG. 1 is a schematic overview of an example utility measuring system100 in which the examples disclosed herein may be implemented. Accordingto the illustrated example of FIG. 1, the utility measuring system 100is to characterize and/or monitor a condition of a utility deliverysystem (e.g., a utility fluid delivery system, a utility delivery node,etc.) 101. The example utility measuring system 100 includes a sensingmodule 102 that is coupled to a pipe 103 (of the utility delivery system101) and includes an analysis module 104, and a first bi-directionalcommunication link 106 that communicatively couples the sensing module102 to an endpoint (e.g., a utility measuring endpoint, a communicationendpoint, a utility endpoint, etc.) 108. The example utility measuringsystem 100 also includes a second bi-directional communication link 110that communicatively couples the endpoint 108 to a server (e.g., aremote server, a data collection facility, etc.) 111. The example server111 includes a head end (e.g., a server gateway, etc.) 112 and a remoteserver 114, both of which are coupled together via a connection 116,such as a file transfer protocol (FTP) in this example.

In this example, the first bi-directional communication link 106 isimplemented as a wired cable and the second bi-directional communicationlink 110 is implemented as a radio frequency (RF) link. However, anyappropriate communication link and/or server/network topographies may beutilized to implement (e.g., a wired or wireless implementation thereof)the communication connections/links 106, 110, 116 instead.

To characterize a condition (e.g., a baseline or current/operatingcondition) of the fluid delivery/utility system 101, the sensing module102 of the illustrated example utilizes a sensor to record or measure(e.g., acoustically measure) multiple recordings (e.g., time-domainrecordings) from a section or portion of the fluid delivery/utilitysystem 101 to define a spectral average, for example. According to theillustrated example, the spectral average is generated based onconverted spectral representations of the recordings.

In this particular example, the sensing module 102 is coupled to a pipeof the fluid delivery/utility system 101. According to the illustratedexample, and as will be discussed in greater detail below in connectionwith FIGS. 3-9, the analysis module 104 performs averaging of known orbaseline recordings to define a spectral average (e.g., a baselinespectral average) that may be used in comparisons with currentrecordings and/or current spectral averages to determine a leakcondition of the fluid delivery/utility system 101, for example.

In some examples, the analysis module 104 packages or encodes thecurrent recordings, spectral representations and/or spectral average(s)(e.g., for later transmission to the remote server 114) into a datapacket. Additionally or alternatively, the analysis module 104 controlsparameters of the sensing module 102 based on current recordingmeasurements (e.g., a measured amplitude of a current recording) oraverages. For example, the analysis module 104 may direct an increase orincrease of a polling frequency and/or control a power mode of thesensing module 102 based on the measurements (e.g., an increased pollingfrequency based on a sudden increase in amplitude of the measurements).

To transmit the recordings, the spectral representations, the spectralaverages and/or determined condition(s) to the example server 111 (e.g.,to the server 111 for later analysis), the sensing module 102 transmitsthe aforementioned data packet to the endpoint 108 via thebi-directional communication link 106 and, in turn, the endpoint 108transmits this data packet to the server 111 via the bi-directionalcommunication link 110. In some examples, the head end 112 then forwardsthe data packet to the remote server 114. In particular, the data packetmay be utilized and/or analyzed at the remote server 114, which may belocated at a utility control center/facility, to convey a condition ofan overall utility node/network and/or direct maintenance crews forrepair work need, for example.

In some examples, the analysis module 104 is implemented on the endpoint108 or the server 111. In some examples, operational functionality ofand/or analysis that is at least partially performed by the analysismodule 104 is distributed across the sensing module 102, the endpoint108 and/or the server 111 (e.g., a distributed analysis/computationtopography). Additionally or alternatively, in some examples, theendpoint 108 is integral with the sensing module 102. In some examples,the sensing module 102 includes circuitry to receive power from theendpoint 108 and/or other external device.

FIG. 2 is a transparent view illustrating the example sensing module 102of the example utility measuring system 100 of FIG. 1. The sensingmodule 102 of the illustrated example includes a housing 202, thebi-directional communication link 106, a coupler (e.g., a pipe clamp)204 and a pipe fitting (e.g., a pipe clamp, a coupling section, etc.)206 that is coupled to the pipe (e.g., a utility pipe, a water pipe,etc.) 103. According to the illustrated example, the coupler 204 aligns,secures and/or couples the sensing module 102 and/or the housing 202 tothe pipe fitting 206 so that measurements or recordings related to thepipe 103 and/or the overall fluid delivery/utility system 101 may beperformed or directed by the sensing module 102.

In operation, fluid generally flows along a longitudinal length of thepipe 103 and through the pipe fitting 206 in this example. In turn, theexample sensing module 102 records and/or analyzes data (e.g., spectraldata) related to this fluid flow to characterize an operating conditionof the utility delivery/consumption system 101. As a result, dataassociated with this recording, which may be in the form of a datapacket (e.g., a spectral data packet, a packet that includes compresseddata), is transmitted to the endpoint 108 of FIG. 1. The communicationlink 106 of the illustrated example provides power to the sensing module102 and may be used to provide commands to and/or control operation ofthe sensing module 102. In some examples, the sensing module 102 may bedirected by the endpoint 108, the head end 112 and/or the remote server114 to increase a polling/sensing rate and/or enter a low power mode,for example.

In some examples, the housing 202 is substantially environmentallysealed (e.g., hermetically sealed). Additionally alternatively, thehousing 202 may be sealed to the coupler 204 in some examples. In otherexamples, the sensing module 102 is directly coupled to the pipe 103 oris defined in the fitting 206.

FIG. 3 is an exploded view of the example sensing module 102 shown inFIGS. 1 and 2. The sensing module 102 of the illustrated exampleincludes a removal grommet 302, the housing 202, the communication link106, a lid 306, a circuit board (e.g., a printed circuit board) 308 thatincludes the analysis module 104, a sensor 310, a housing portion 312, amechanical coupler or fastener 314 and a mount assembly 316, which is tobe coupled to the coupler 204 shown in FIG. 2 via the fastener 314 inthis example.

According to the illustrated example, to measure or recordrecordings/data associated with fluid delivery operation, the sensor310, which is implemented as an acoustic sensor in this example, isoperatively coupled to the pipe fitting 206 and/or the pipe 103. In thisexample, the sensor 310 records and/or measures recordings correspondingto the fluid delivery/utility system 101. In this example, the sensor310 records vibration signals (e.g., vibrations, vibration recordings,vibration waveforms, processed vibration signals, sound, etc.) to beused in characterizing a leak condition of the pipe 103 and/or therespective fluid delivery/utility system 101. In particular, the examplesensor 310 may record at least one recording based on a pre-defined ordesignated time period. In some examples, the recording may be converted(e.g., transformed) to a spectral representation to characterize thevibrational and/or acoustic signal over a frequency domain (e.g.,relating amplitude of the vibrational and/or acoustic signal tofrequency). In particular, the recording may be converted from atime-domain sample to a frequency domain.

In this example, to characterize, analyze and/or determine a leakcondition, circuitry of the circuit board 308, which includes theanalysis module 104 shown in FIG. 1, receives a plurality of baselinerecordings measured at the sensor 310. In turn, the circuit board 308utilizes this plurality of baseline recordings to be converted tocorresponding spectral representations for generating a baselinespectral average and/or baseline noise signature. Such a process isdescribed in detail below in connection with FIG. 6. In this example,the baseline spectral average is transmitted to the remote server 114 asa first data packet.

In this example, the sensor 310 also measures current recordingssubsequent to the generation of the baseline spectral average and, inturn, converts the current recordings to respective spectralrepresentations that are used to generate a current spectral average,which is also generated using the process described below in connectionwith FIG. 6. In particular, the current spectral average may begenerated and stored over a periodic basis (e.g., every day, every week,etc.). In this example, the current spectral average is transmitted tothe remote server 114 as a second data packet. According to theillustrated example, the current spectral average is then compared tothe baseline spectral average by the remote server 114 to determine theleak condition.

Additionally or alternatively, the current spectral average is comparedto a known signature (e.g., a known leak signature), which may becharacterized in the time domain or frequency domain. In particular,this known signature may correspond to an expected spectral signaturebased on a known parameter. For example, a square wave in the timedomain may be representative a spinning disk of a utility water meter.In another example, a pronounced 60 or 120 Hertz (Hz) in the frequencydomain may indicate electrical/power noise. Additionally oralternatively, the known signatures may take into account geometry(e.g., diameter, cross-sectional profile, length etc.) and/ormaterial(s) (e.g., plastic, metal, etc.) of pipes. In some examples, thecircuit board 308 stores the aforementioned known signature, which maybe received and/or downloaded from the remote server 114. Additionallyor alternatively, the baseline spectral average is subtracted from thecurrent spectral average prior to making this comparison. In someexamples, the current spectral average is compared to historicalspectral averages and/or recordings.

In examples where the determination of the leak condition is performedsolely at the circuit board 308, to transfer the determined leakcondition so that appropriate actions may be taken (e.g., servicerelated actions), the circuit board 308 transmits data associated withthe determined leak condition to the end point 108 via the communicationlink 106. In some examples, the circuit board 308 encodes and/orcompresses this data to be included in a data packet for transmission.

While the sensor 310 of the illustrated example is described as avibrational sensor in this example, the sensor 310 may be implemented asany other appropriate sensor type such as a pressure sensor, a soundsensor and/or a temperature sensor, for example, may be used. In someexamples, the sensor 310 includes an accelerometer, which may beimplemented as piezo-electric accelerometer, a micro-electromechanicalsystem (MEMS) device, a ceramic accelerometer, or any other appropriatetype of accelerometer, etc. In some examples, the sensor 310 is integralwith the circuit board 308.

FIG. 4 is a plot 400 representing example signal acquisition andanalysis in accordance with the teachings of this disclosure. Inparticular, adaptive sampling of obtaining recordings that is performedby the sensor 310 to enable the circuit board 308 to generate a spectralaverage a spectral noise signature), which may be used to define abaseline spectral average or a current spectral average, over differentrecording session types is demonstrated. Further, the example plot 400also illustrates example sensor initiation that may be implemented inthe examples disclosed herein. In the view of FIG. 4, a legend 401representing signal recording classifications and a time scale 402representing a time of day are shown.

According to the illustrated example, a first example recording session404 is shown over a portion of the time scale 402. A signal plot (e.g.,a measured plot, a measured recording, a recorded section, etc.) 406 ofrecording measurements is characterized by its regions or portions 406a, 406 b and 406 c. In the region 406 a, the signal plot 406 exhibitsrelatively flat behavior. As can be seen in the region 406 b of FIG. 4,the signal plot 406 decreases and crosses a threshold (e.g., a setpoint) and/or threshold range, and generally levels out and/or flattensalong the region 406 c. Accordingly, a corresponding recording mode 408shows an increase in polling of the sensor 310 (e.g., an increased dataacquisition rate of the sensor 310) based on a transition from theregion 406 b to the region 406 c, for example. In this particularexample, an amplitude above a threshold level triggers a recordingsession to begin and subsequent movement across one or more thresholdscauses a polling rate to vary.

A second example recording session 410 involves a signal plot 412remaining within a threshold range and, in turn, a polling frequencyshown in a corresponding recording mode 414 does not change, in contrastto the example recording session 404. In this example, approximately10-20 recordings (e.g., sixteen recordings) are needed to develop thespectral average and/or noise profile. However, any appropriate numberof samples may be used since the appropriate number of recordings can bedependent on application or use.

A third example recording session 420 shows a signal plot 422 havingcorresponding noisy regions 422 a and 422 b. In this example, acorresponding recording mode 424 indicates noisy recordings 426, both ofwhich are discarded/rejected. Accordingly, to obtain a proper number ofrecordings in this example, the sensor 310 is directed to measure and/orappend recordings 428 based on the deletion of the noisy recordings 426.

In some examples, a recording session is initiated based on a measuredamplitude exceeding a threshold (e.g., an absolute peak amplitude in thetime domain may be utilized). In some examples, when periods pertainingto relatively low amplitude measurements (e.g., quiet periods) aresubsequently followed up with a relatively large amplitude signal, arecording session is initiated.

In some examples, if a recording session ends up noisy and/or faulty(e.g., the recordings 426), configuration data and/or operatingparameters of the sensor 310 (shown in FIG. 3) may be adjusted by thecircuit board 308 to increase a probability of less-noisy signalsmeasured at the sensor 310, for example. In some examples, indication ofa faulty recording session and/or incomplete recording data (e.g., alower number of recordings than a requisite number, a disconnect, etc.),may trigger a bias flag to be transmitted (e.g., added or encoded to adata packet that is transmitted).

In some examples, if a recording session is successful, the associateddata and/or recording session data is converted to and/or analyzedwithin the frequency domain to define at least one spectralrepresentation. Additionally or alternatively, associated spectralenergy is examined and may be weighted to an array of the followingvalues: [−1 0 1], for example. In particular, for the spectral energymeasurements with central tendencies (e.g., falling within expectedstatistical bounds), the corresponding spectral representation and/orrecording is given a weighting value close to 0 (e.g., a value of 0).Accordingly, higher spectral energy recordings and/or correspondingspectral representations may be weighted with a positive value while, incontrast, lower spectral energy recordings and/or corresponding spectralrepresentations may be weighted with a negative value.

In some examples, root-mean-square (rms) data associated with therecordings is analyzed and/or examined. In such examples, if the rmsvariance is low, a minimal change in signal amplitude may be determined.Additionally or alternatively, summing a combination of spectralweighting as well as rms variance may indicate a value indicating datafit quality (e.g., a “goodness” of fit).

In some examples, recording times and/or time period(s) of the day torecord are selected based on a time of day, one or more previousrecordings and/or one or more previously generated spectralrepresentations. In such examples, the selected recording times (e.g.,time of day) may be transmitted to an external device (e.g., so that theexternal device may trigger the leak detection sensor 310 and/or thesensing module 102 to turn on).

Any aspect described above with respect to any of the example recordingsessions 404, 410 and 420 may be used in collecting recordings and/ordeveloping spectral representations to generate spectral averages thatpertain to a baseline (e.g., known condition) spectral average or anew/current spectral average (e.g., today's spectral average from 2 amto 4 am, etc.). In particular, the collected and/or sorted recordingsmay be processed using any combination of the techniques described aboveto generate the aforementioned averaged recordings.

In some examples, fault-indicating recordings indicating abnormaloperation, improper installation and/or misplacement of the sensor 310and/or the sensing module 102 trigger a warning and/or flag to indicatethat the recordings obtained are faulty and/or may result in generationof faulty spectral representations and/or averages. Additionally oralternatively, these fault indicating recordings may trigger a reset ofthe sensor 310 and/or the sensing module 102.

FIG. 5 is a graph 500 representing a signal recording comparison thatmay be implemented in the examples disclosed herein. In particular, thiscomparison may be performed by the remote server 114 or the circuitboard 308. According to the illustrated example, the graph 500 shows anexample comparison of a baseline spectral average to a current spectralaverage (e.g., recordings averaged within the last day). According tothe illustrated example of FIG. 5, the graph 500 includes a legend 501,a horizontal axis 502, which indicates an acoustic amplitude, and ahorizontal axis 504 that indicates an acoustic frequency in hertz (Hz).In this example, the current spectral average and the baseline spectralaverage are characterized, compared and/or analyzed in the frequencydomain.

In the example graph 500 of FIG. 5, a current spectral average plot 506is shown in relation to an averaged baseline spectral plot (e.g., anacoustic profile corresponding to the pipe 103 of FIGS. 1 and 2) 508. Inthis example, a difference between current spectral average plot 506 andaveraged baseline spectral plot 508 is used to determine a leakcondition of a corresponding fluid delivery system. In other words, asignificant difference, variance and/or a degree to which the currentspectral average plot 506 and the averaged baseline spectral plot 508differ may indicate an overall condition of the fluid delivery systemhis example, the averaged baseline spectral plot 508 is subtracted(e.g., via a spectral subtraction) from the current spectral averageplot 506 to determine this difference.

In some examples, the difference between the current spectral averageplot 506 and averaged baseline spectral plot 508 is determined and/orcalculated by taking an integral of a difference between the currentspectral average plot 506 and averaged baseline spectral plot 508 (e.g.,an integral average of the differences). In other words, sum areas ofthe current spectral average plot 506 and averaged baseline spectralplot 508 may be subtracted from one another to determine the difference.Additionally or alternatively, overall shapes of waveforms correspondingto the current spectral average plot 506 and the averaged baselinespectral plot 508 are compared to one another to determine thisdifference.

In some examples, the difference between the current spectral averageplot 506 and the averaged baseline spectral plot 508 is determined byquantifying a maximum numerical difference in amplitude. In particular,the maximum difference at a certain frequency between the currentspectral average plot 506 and the averaged baseline spectral plot 508may be selected to determine a leak condition, for example.

Additionally or alternatively, at least one peak and/or characteristicshape of the current spectral average plot 506 is tracked as it shiftsover time (e.g., while generally retaining aspects of its characteristicwaveform) and this shift is taken into account when comparing thecurrent spectral average plot 506 to the averaged baseline spectral plot508. In such examples, spectral tracking can be effective at trackingpeak and/or waveform shifts based on edges of the current spectralaverage plot 506 that are clearly defined in the frequency domain.

In some examples, time-domain recordings corresponding to the averagedbaseline spectral plot 508 and/or the current spectral average plot 506are processed/transformed with a fast Fourier transform (FFT) for latercharacterization and/or comparison.

FIG. 6 illustrates an example result of an averaging process that may beimplemented in the examples disclosed herein. In particular, the exampleof FIG. 6 demonstrates how multiple recordings that are converted tospectral representations can then be averaged to generate a spectralaverage, which may correspond to either a baseline spectral average or acurrent spectral average. In other examples, the recordings and/orspectral representations may be normalized (e.g., amplitudes representedas ratios of one) instead of averaged.

As can be seen in FIG. 6, spectral representations 602, 604 and 606 areshown, which correspond to first, second and sixteenth recordings,respectively, in this example. An averaged frequency plot 602 a, whichcorresponds to the first spectral representation 602, is similar and/oridentical to the averaged frequency plot 602 a because there have notyet been any other samples to average with.

A second averaged frequency plot 604 a corresponds to an averageincluding the spectral representation 602 and the spectralrepresentation 604. However, in this example, not enough measurementshave been taken to generate a fully-defined baseline spectral noiseaverage.

A third averaged frequency plot 606 a corresponds to sixteen recordings,which include the spectral representations 602, 604 and 606, as well asadditional spectral representations not shown. According to theillustrated example, the third averaged frequency plot 606 a exhibits arelative flat region 608 and a well-defined peak 610 (between 300 and400 Hz) that is disposed between portions of the flat region 608. As aresult, the corresponding spectral average is well-defined (e.g.,fully-defined).

Accordingly, the third averaged frequency plot 606 a may be used as anaveraged baseline spectral average to be compared to a current spectralaverage to determine a leak condition. Alternatively, the third averageplot 606 a may define the current spectral average that is compared toanother baseline spectral average. In this example, taking a significantand/or requisite amount of recordings enables noise (e.g., environmentalnoise, random noise, etc.) to be effectively canceled out and/orsmoothed out. In other words, the randomness of multiple recordingsand/or their associated spectral representations effectively removesrandom noise from inherent or characteristic system noise, therebyenabling very accurate baseline or known scenario characterization.While sixteen recordings/signatures are described in this example, anyappropriate number of recordings may be used based on accuracy needsand/or inherent characteristics of a respective fluid delivery system.

In some examples, different recordings and/or associated spectralrepresentations are weighted differently. In particular, a recording,which may be associated with a known baseline time period, historicaland/or older in time, may have a greater weighting factor than lateradded recordings, for example. In some examples, similar recordingsand/or associated spectral representations are grouped together (e.g.,assigned a particular number based on similar amplitudes and/orwaveforms), thereby defining grouped recordings or representations.Additionally or alternatively, these grouped recordings or spectralrepresentations are designated a number or label (e.g., 127) that can beincremented based on increasing amplitudes (e.g., 127+1). In theseexamples, these grouped recordings or spectral representations may beaveraged or normalized together to define a grouped spectral average.

FIG. 7 is a schematic overview of the example analysis module 104 ofFIG. 1 that may be used to implement the examples disclosed herein. Theexample analysis module 104 of the illustrated example includes aspectral signal calculator 702, which includes a spectral signalpre-processor 704, and a spectral signal analyzer 706 and a conditioncomparator 708.

The example analysis module 104 also includes a recording or signaturestorage 709 and an encoder/transmitter 710 that is communicativelycoupled to the spectral signal calculator 702 via a communication line714. Further, the example encoder/transceiver 710 is communicativelycoupled to the condition comparator 708 via a communication line 712.Also, the sensor 310 of FIG. 3 is shown communicatively coupled to thespectral signal pre-processor 704 via a communication line 716. Further,the encoder/transceiver 710 is communicatively coupled to the endpoint108 via a communication line 718.

To obtain a requisite amount of recordings (e.g., time-domainrecordings) to be converted to associated spectral representations tosufficiently define or generate a spectral average, the sensor 310 ofthe illustrated example provides a plurality of recordings to thespectral signal pre-processor 704 so that each recording of theplurality of recordings is converted to a respective spectralrepresentation. In turn, the spectral signal pre-processor 704determines whether the spectral representations and/or the recordingsmeet noise and/or waveform requirements and collects a requisite number(e.g., 10-20, 100, 1000, etc.) of the recordings from the sensor 310that are needed to generate a well-defined spectral average (e.g., acurrent spectral average, a baseline spectral average). Additionally oralternatively, the spectral signal pre-processor 704 directs and/orcontrols operating parameters of the sensor 310. For example, thespectral signal pre-processor 704 may control a polling frequency and/orpower consumption mode (e.g., a low power mode, etc.) of the sensor 310based on at least one recording and/or an associated spectralrepresentation. Accordingly, the spectral signal pre-processor 704 actsas power controller of the sensor 310 in these examples. Additionally oralternatively, the sensor 310 is to exit an off power state based upon asupply of power from an external device (e.g., the endpoint 108) and toreturn to the off power state after a data packet is transmitted, forexample.

To generate the spectral average/signature, the example spectral signalanalyzer 706 averages multiple spectral representations by performingaveraging techniques shown and described in connection with the exampleof FIG. 6. In this example, the spectral signal analyzer 706 receivesmultiple spectral representations of recordings that have been sortedand/or pre-processed by the example spectral signal pre-processor 704 togenerate the spectral average. Additionally or alternatively, thesepre-processed recordings, the respective spectral representations and/orthe generated spectral average is stored in the recording storage 709and retrieved therefrom (e.g., retrieved to be packaged with a latermeasured current spectral average).

To determine a leak condition and/or operational status, the conditioncomparator 708 of the illustrated example compares the current spectralaverage to the baseline spectral average. In this example, thisdetermination is based on a degree to which the current spectral averagevaries from the baseline spectral average. Once this leak condition hasbeen determined, the condition comparator 708 transmits the determinedleak condition and/or a packet containing the current spectral averageto the encoder/transceiver 710 which, in turn, forwards this data to theendpoint 108 of FIG. 1. In some examples, the baseline spectral averageis updated to be the current spectral average. For example, similarconsecutive current spectral averages and/or significant differentialchanges in current spectral averages may cause this update to occur.

In some examples, the current spectral average is compared to ahistorical recording and/or spectral average to determine the leakcondition. Additionally or alternatively, a previously saved recordingis retrieved from the recording storage 709 and/or spectral data packetsare retrieved or transmitted for leak condition determinations.

In some examples, overall energy of a spectral average may be taken intoaccount for leak condition determination. In such examples, seasonalvariations, noise variations, etc. may be taken into account.

Additionally or alternatively, in some examples, known signature(s) arestored in the recording storage 709 and may be retrieved and transmitted(e.g., transmitted upon request, etc.). In such examples, the conditioncomparator 708 compares the known signature to the current recording,spectral representation and/or spectral average. The known signature maybe selected from the storage 709 based on one or more operatingparameters, which may include, but is not limited to, pipe diameter,pipe structure, pipe material(s), overall utility delivery designparameters, etc.

While an example manner of implementing the example analysis module 104of FIGS. 1 and 7 is illustrated in FIG. 7, one or more of the elements,processes and/or devices illustrated in FIG. 7 may be combined, divided,re-arranged, omitted, eliminated and/or implemented in any other way.Further, the example spectral signal pre-processor 704, the examplespectral signal analyzer 706, the example condition comparator 708, theexample encoder/transmitter 710 and/or, more generally, the exampleanalysis module 104 of FIGS. 1 and 7 may be implemented by hardware,software, firmware and/or any combination of hardware, software and/orfirmware. Thus, for example, any of the example spectral signalpre-processor 704, the example spectral signal analyzer 706, the examplecondition comparator 708, encoder/transmitter 710 and/or, moregenerally, the example analysis module 104 could be implemented by oneor more analog or digital circuit(s), logic circuits, programmableprocessor(s), application specific integrated circuit(s) (ASIC(s)),programmable logic device(s) (PLD(s)) and/or field programmable logicdevice(s) (FPLD(s)). When reading any of the apparatus or system claimsof this patent to cover a purely software and/or firmwareimplementation, at least one of the example, spectral signalpre-processor 704, the example spectral signal analyzer 706, the examplecondition comparator 708, and/or the example encoder/transmitter 710is/are hereby expressly defined to include a tangible computer readablestorage device or storage disk such as a memory, a digital versatiledisk (DVD), a compact disk (CD), a Blu-ray disk, etc. storing thesoftware and/or firmware. Further still, the example analysis module 104of FIGS. 1 and 7 may include one or more elements, processes and/ordevices in addition to, or instead of, those illustrated in FIG. 7,and/or may include more than one of any or all of the illustratedelements, processes and devices.

A flowchart representative of example machine readable instructions forimplementing the analysis module 104 of FIG. 7 is shown in FIG. 8. Inthis example, the machine readable instructions comprise a program forexecution by a processor such as the processor 912 shown in the exampleprocessor platform 900 discussed below in connection with FIG. 9. Theprogram may be embodied in software stored on a tangible computerreadable storage medium such as a CD-ROM, a floppy disk, a hard drive, adigital versatile disk (DVD), a Blu-ray disk, or a memory associatedwith the processor 912, but the entire program and/or parts thereofcould alternatively be executed by a device other than the processor 912and/or embodied in firmware or dedicated hardware. Further, although theexample program is described with reference to the flowchart illustratedin FIG. 8, many other methods of implementing the example analysismodule 104 may alternatively be used. For example, the order ofexecution of the blocks may be changed, and/or some of the blocksdescribed may be changed, eliminated, or combined.

As mentioned above, the example processes of FIG. 8 may be implementedusing coded instructions (e.g., computer and/or machine readableinstructions) stored on a tangible computer readable storage medium suchas a hard disk drive, a flash memory, a read-only memory (ROM), acompact disk (CD), a digital versatile disk (DVD), a cache, arandom-access memory (RAM) and/or any other storage device or storagedisk in which information is stored for any duration (e.g., for extendedtime periods, permanently, for brief instances, for temporarilybuffering, and/or for caching of the information). As used herein, theterm tangible computer readable storage medium is expressly defined toinclude any type of computer readable storage device and/or storage diskand to exclude propagating signals and to exclude transmission media. Asused herein, “tangible computer readable storage medium” and “tangiblemachine readable storage medium” are used interchangeably. Additionallyor alternatively, the example processes of FIG. 8 may be implementedusing coded instructions (e.g., computer and/or machine readableinstructions) stored on a non-transitory computer and/or machinereadable medium such as a hard disk drive, a flash memory, a read-onlymemory, a compact disk, a digital versatile disk, a cache, arandom-access memory and/or any other storage device or storage disk inwhich information is stored for any duration (e.g., for extended timeperiods, permanently, for brief instances, for temporarily buffering,and/or for caching of the information). As used herein, the termnon-transitory computer readable medium is expressly defined to includeany type of computer readable storage device and/or storage disk and toexclude propagating signals and to exclude transmission media. As usedherein, when the phrase “at least” is used as the transition term in apreamble of a claim, it is open-ended in the same manner as the term“comprising” is open ended.

The example method of FIG. 8 begins as the leak detection sensor 301that has been coupled to an installation (e.g., a previously installedinstallation) that is currently being operated. In this example, theinstallation is being used as a node in a utility network and/or systemto provide fluid (e.g., water or gas) to utility customers and abaseline spectral average has already been calculated and uploaded tothe remote server 114 by the analysis module 104.

In this example, the spectral pre-processor 704 determines whether asession (e.g., a recording session, a characterization session, etc.)should be active (block 801). If the session is to be active and/orcontinue (block 801), control of the process proceeds to block 802.Otherwise, the process ends (e.g., the recording session is notcontinued or initiated).

According to the illustrated example, it is then determined whetherrecordings are to be added and/or obtained (block 802). In particular,the spectral signal pre-processor 704 and/or the spectral signalanalyzer 706 may determine whether the recordings are to be obtained.This determination may be based on whether a new spectral average isneeded (e.g., whether a spectral average of the today's recordings isdesired) or whether a new baseline spectral average should or needs tobe defined. If additional recordings are to be added (block 802),control of the process proceeds to block 804. Otherwise, the processproceeds to block 816.

If at least one additional recording is to be added/obtained (block802), a recording is made (block 804). In this example, the sensor 301records a time domain signal/recording and/or characteristics in thetime domain.

According to the illustrated example, time domain statistics of therecording are computed by the spectral signal pre-processor 704, forexample (block 806). For example, numerous quantitative attributes ofthe recording such as peaks and/or signal patterns, for example, in thetime domain may be recorded and/or characterized. In some examples, dataassociated with these time-domain characteristics is stored in therecording storage 709 for later transmission (e.g., encoded in a packetalong with the spectral average data).

A spectral representation of the time-domain recording iscomputed/calculated by the example spectral signal analyzer 706 and/orthe spectral signal pre-processor 704 (block 808). In other words, thetime domain signal recording is converted into a frequency domainrecording via a transformation. In this example, this conversion occursvia an FFT analysis of the time domain signal, thereby defining aspectral representation. In some examples, a power spectral densitycalculation is used. In some examples, wavelet filtering is performed.

Next, the spectral representation and/or data associated with thespectral representation is saved (block 810). In this example, thespectral representation is saved onto the recording storage 709.

In some examples, a recording counter (e.g., a counter to record anumber of recordings) is incremented (block 812). In such examples, thecounter is used to determine whether a minimum number of recordings havebeen obtained (e.g., 16 recordings, 50 recordings, etc.).

According to the illustrated example, recording parameters of the sensor310 may be updated and/or varied (block 813). In particular, thespectral signal pre-processor 704 and/or the spectral signal analyzer706 of the illustrated example may vary a polling frequency and/or powerdraw mode of the sensor 310 based on at least one incoming currentrecording and/or its associated spectral representation.

In some examples, this polling frequency may be varied based on a pipeconfiguration (e.g., diameter, material, coupling implementation), typeof environment (e.g., residential, valve, industrial, commercial, etc.),or any appropriate environment. Additionally or alternatively, sensor310 is directed to perform measurements at a defined time period (e.g.,a few minutes, a few hours, a few days, a few months, etc.) aftercertain events and/or daily use patterns.

In some examples, a return/transmit recording status is transmitted bythe encoder/transceiver 710, for example (block 814) and the processreturns to block 801.

In some examples, recordings and/or associated spectral representationsare selected and/or sorted (block 816). In particular, the spectralsignal pre-processor 704 of the illustrated example may remove noisyspectral representations and/or append newer spectral representations.Additionally or alternatively, spectral representations are grouped bysimilarity (e.g., a similarity in peak amplitude and/or waveform shape,etc.).

According to the illustrated example, session/current spectral dataand/or average is computed and saved (block 818). In particular, aspectral average of the converted spectral representations is generatedand/or computed.

In some examples, the baseline spectral average is compared to thecurrent session spectral average to determine a leak condition (block820). In particular, the condition comparator 708 and/or the remoteserver 114 may perform this comparison. In this example, the baselinespectral average is subtracted from the current session spectralaverage. In other examples, an integral average between the currentspectral average and the baseline spectral average may be used. In someexamples, this comparison is at least partially based on a knownspectral signature that is stored in the signature storage 709.Additionally or alternatively, differences (e.g., numerical differences)in amplitude peaks at respective frequencies and/or frequency ranges arecompared.

In some examples, the spectral average is compared to a known signaturefrom a database (block 822). This comparison may be performed todetermine a leak condition. In particular, the database may be stored onthe recording storage 709 and/or the remote server 114.

In some examples, the collected spectral average data is compressed(block 824). In such examples, the spectral signal analyzer 706 and/orthe spectral signal pre-processor 704 may perform the compression. Forexample, an A-law compression algorithm may be used. Additionally oralternatively, the collected spectral average data is grouped with otherrecordings and/or categorized into a known group of recordings/spectralrepresentation to save disk storage space some examples, the compressionmay be performed to reduce necessary transmissions to the endpoint 108,thereby saving power and/or battery use.

According to the illustrated example, a data packet is encoded, computedand/or generated (block 826). In this example, the encoder/transceiver710 packages the current spectral average for later transmission. Inparticular, the current session spectral average is encoded into a datapacket. Further, other attribute information and/or characteristics suchas time-domain characteristics may be encoded into the data packet. Insome examples, temperature data (e.g., a time-temperature history) isalso encoded into the data packet for additional analysis at the remoteserver 114. In some examples, a bias flag is also added to the datapacket when there are a number of recordings or spectral representationsbelow a threshold number.

In some examples, the recording storage 709 stores the data packet(block 828). For example, the data packet may be stored for latertransmission and/or to be combined with other data later and/or forlater transmission when the leak detection sensor is turned on.

According to the illustrated example, the data packet is transmitted(block 830) and the process ends. In particular, the exampleencoder/transceiver 710 transmits the aforementioned data packet to theremote server 114 via the endpoint 108.

FIG. 9 is a block diagram of an example processor platform 900 capableof executing the instructions of FIG. 8 to implement the analysis module104 of FIGS. 1 and 7. The processor platform 900 can be, for example, aserver, a personal computer, a mobile device (e.g., a cell phone, asmart phone, a tablet such as an iPad™), a personal digital assistant(PDA), an Internet appliance, a DVD player, a CD player, a digital videorecorder, a Blu-ray player, a gaming console, a personal video recorder,a set top box, or any other type of computing device.

The processor platform 900 of the illustrated example includes aprocessor 912. The processor 912 of the illustrated example is hardware.For example, the processor 912 can be implemented by one or moreintegrated circuits, logic circuits, microprocessors or controllers fromany desired family or manufacturer.

The processor 912 of the illustrated example includes a local memory 913(e.g., a cache). The example processor also includes the examplespectral signal pre-processor 704, the example spectral signal analyzer706, condition comparator 708 and the encoder/transceiver 710. Theprocessor 912 of the illustrated example is in communication with a mainmemory including a volatile memory 914 and a non-volatile memory 916 viaa bus 918. The volatile memory 914 may be implemented by SynchronousDynamic Random Access Memory (SDRAM), Dynamic Random Access Memory(DRAM), RAMBUS Dynamic Random Access Memory (RDRAM) and/or any othertype of random access memory device. The non-volatile memory 916 may beimplemented by flash memory and/or any other desired type of memorydevice. Access to the main memory 914, 916 is controlled by a memorycontroller.

The processor platform 900 of the illustrated example also includes aninterface circuit 920. The interface circuit 920 may be implemented byany type of interface standard, such as an Ethernet interface, auniversal serial bus (USB), and/or a PCI express interface.

In the illustrated example, one or more input devices 922 are connectedto the interface circuit 920. The input device(s) 922 permit(s) a userto enter data and commands into the processor 912. The input device(s)can be implemented by, for example, an audio sensor, a microphone, acamera (still or video), a keyboard, a button, a mouse, a touchscreen, atrack-pad, a trackball, isopoint and/or a voice recognition system.

One or more output devices 924 are also connected to the interfacecircuit 920 of the illustrated example. The output devices 924 can beimplemented, for example, by display devices (e.g., a light emittingdiode (LED), an organic light emitting diode (OLED), a liquid crystaldisplay, a cathode ray tube display (CRT), a touchscreen, a tactileoutput device, a printer and/or speakers). The interface circuit 920 ofthe illustrated example, thus, typically includes a graphics drivercard, a graphics driver chip or a graphics driver processor.

The interface circuit 920 of the illustrated example also includes acommunication device such as a transmitter, a receiver, a transceiver, amodem and/or network interface card to facilitate exchange of data withexternal machines e.g., computing devices of any kind) via a network 926(e.g., an Ethernet connection, a digital subscriber line (DSL), atelephone line, coaxial cable, a cellular telephone system, etc.).

The processor platform 900 of the illustrated example also includes oneor more mass storage devices 928 for storing software and/or data.Examples of such mass storage devices 928 include floppy disk drives,hard drive disks, compact disk drives, Blu-ray disk drives, RAIDsystems, and digital versatile disk (DVD) drives.

The coded instructions 932 of FIG. 8 may be stored in the mass storagedevice 928, in the volatile memory 914, in the non-volatile memory 916,and/or on a removable tangible computer readable storage medium such asa CD or DVD.

From the foregoing, it will be appreciated that the above disclosedmethods, apparatus and articles of manufacture provide an effective andpower-efficient manner of accurately determining a leak condition bygenerating a spectral average based on organizing and/or analyzingmultiple recordings (e.g., multiple baseline recordings). In particular,the generated spectral averages that are not affected by ambient and/orinherent noise characteristics of corresponding systems (e.g., utilitysystems) and may be compared to a current recording to determine theleak condition. The examples disclosed herein also enable adaptive datasampling, which can reduce power consumption, especially in battery-rundevices such as some remote sensors (e.g., remote acoustic leakdetection sensors) to obtain recordings.

Although certain example methods, apparatus and articles of manufacturehave been disclosed herein, the scope of coverage of this patent is notlimited thereto. On the contrary, this patent covers all methods,apparatus and articles of manufacture fairly falling within the scope ofthe claims of this patent. While the examples disclosed herein are shownin relation to utility metering systems, any of the examples disclosedherein, including the example processing and/or analysis techniques, maybe used in any appropriate application(s).

What is claimed is:
 1. An apparatus comprising: a leak detection sensorto record a plurality of recordings; a memory; a processor to: convertat least two recordings of the plurality of recordings to correspondingspectral representations, wherein the at least two recordings areseparated by a time gap, calculate a spectral average based, at least inpart, on the spectral representations, store the spectral average to thememory, and generate a data packet based, at least in part, on thespectral average; and a transceiver to transmit the data packet toanother device to compare the spectral average to a baseline spectralaverage to determine a leak condition of a fluid delivery system.
 2. Theapparatus as defined in claim 1, wherein the another device is a remoteserver or an endpoint coupled to a utility meter.
 3. The apparatus asdefined in claim 1, wherein the leak condition corresponds to a pipe ofthe fluid delivery system.
 4. The apparatus as defined in claim 1,wherein the comparison of the spectral average to the baseline spectralaverage includes subtracting the baseline spectral average from thespectral average.
 5. The apparatus as defined in claim 1, wherein theprocessor is to reject one or more of the plurality of recordings priorto calculating the spectral average.
 6. The apparatus as defined inclaim 5, wherein the processor is to direct the leak detection sensor torecord additional recordings based, at least in part, on the rejectionof the one or more of the plurality of recordings.
 7. The apparatus asdefined in claim 5, wherein the processor is to reject the one or moreof the plurality of recordings based on a corresponding noise level. 8.The apparatus as defined in claim 1, wherein the processor is to updatethe baseline spectral average based, at least in part, on the spectralaverage.
 9. The apparatus as defined in claim 1, wherein the processoris to select one or more times to record the plurality of recordingsbased, at least in part, on one or more of a time of day, one or moreprevious recordings, and one or more previous spectral representations.10. The apparatus as defined in claim 9, wherein the processor is totransmit the selected one or more times to record the plurality ofrecordings to an external device.
 11. The apparatus as defined in claim1, wherein the leak detection sensor includes an accelerometer.
 12. Theapparatus as defined in claim 1, wherein the processor is to exit an offpower state based upon a supply of power from an external device and toreturn to the off power state after the transceiver transmits the datapacket.
 13. The apparatus as defined in claim 1, wherein the processoris to encode a bias flag into the data packet based, at least in part,on a number of recordings of the plurality of recordings being less thana threshold number.
 14. A fluid utility measuring device including theapparatus as defined in claim
 1. 15. The apparatus as defined in claim1, wherein the processor is to: determine a spectral energy of at leastone spectral representation of the spectral representations; and apply aweighting factor to the at least one spectral representation based onthe spectral energy, wherein the spectral average is calculated based,at least in part, on the weighting factor.
 16. A method comprising:recording, at a leak detection sensor, a plurality of recordings;converting, at a processor, at least two recordings of the plurality ofrecordings to corresponding spectral representations, wherein the atleast two recordings are separated by a time gap; calculating, at theprocessor, a spectral average based, at least in part, on the spectralrepresentations; generating, at the processor, a data packet based, atleast in part, on the spectral average; and transmitting the data packetto another device to compare the spectral average to a baseline spectralaverage to determine a leak condition of a fluid delivery system. 17.The method as defined in claim 16, wherein the another device is aremote server or an endpoint coupled to a utility meter.
 18. The methodas defined in claim 16, wherein comparing the spectral average to thebaseline spectral average includes subtracting the baseline spectralaverage from the spectral average.
 19. The method as defined in claim16, further including rejecting one or more of the plurality ofrecordings prior to calculating the spectral average.
 20. The method asdefined in claim 19, further including directing the leak detectionsensor to record additional recordings based, at least in part, on therejection of the one or more of the plurality of recordings.
 21. Themethod as defined in claim 16, further including updating the baselinespectral average based, at least in part, on the spectral average. 22.The method as defined in claim 16, further including selecting one ormore times to record the plurality of recordings based, at least inpart, on one or more of a time of day, one or more previous recordings,and one or more previous spectral representations.
 23. The method asdefined in claim 22, further including transmitting the selected one ormore times to record the plurality of recordings to an external device.24. A non-transitory tangible machine readable medium comprisinginstructions, which when executed, cause a processor to at least:convert at least two recordings of a plurality of recordings tocorresponding spectral representations, wherein the at least tworecordings are separated by a time gap; calculate a spectral averagebased, at least in part, on the spectral representations; generate adata packet based, at least in part, on the spectral average, and causea transceiver to transmit the data packet to another device to comparethe spectral average to a baseline spectral average to determine a leakcondition of a fluid delivery system.
 25. The non-transitory machinereadable medium as defined in claim 24, wherein the instructions causethe processor to reject one or more of the plurality of recordings priorto calculating the spectral average.
 26. The non-transitory machinereadable medium as defined in claim 24, wherein the instructions causethe processor to update the baseline spectral average based, at least inpart, on the spectral average.
 27. The non-transitory machine readablemedium as defined in claim 24, wherein the instructions cause theprocessor to select one or more times to record the plurality ofrecordings based, at least in part, on one or more of a time of day, oneor more previous recordings, and one or more previous spectralrepresentations.